Life-course social disparities in body mass index trajectories across adulthood: cohort study evidence from China health and nutrition survey

Background The social disparities in obesity may originate in early life or in adulthood, and the associations of socioeconomic position (SEP) with obesity could alter over time. It is unclear how lifetime-specific and life-course SEP influence adult obesity development in China. Methods Based on the China Health and Nutrition Survey (CHNS), three SEP-related indicators, including the father’s occupational position and the participant’s education and occupational position, were obtained. The life-course socioeconomic changes and a cumulative SEP score were established to represent the life-course SEP of the participants in the study. The growth mixture modeling was used to identify BMI trajectories in adulthood. Multinomial logistic regression was adopted to assess the associations between SEP and adult BMI trajectories. Results A total of 3,138 participants were included in the study. A positive correlation was found between the paternal occupational position, the participants’ occupational position, education, and obesity in males, whereas an inverse correlation was observed among females. Males who experienced social upward mobility or remained stable high SEP during the follow-up had 2.31 and 2.52-fold risks of progressive obesity compared to those with a stable-low SEP. Among females, stable high SEP in both childhood and adulthood was associated with lower risks of progressive obesity (OR = 0.63, 95% CI: 0.43–0.94). Higher risks of obesity were associated with the life-course cumulative SEP score among males, while the opposite relationship was observed among females. Conclusions The associations between life-course SEP and BMI development trajectories differed significantly by gender. Special emphasis should be placed on males experiencing upward and stable high socioeconomic change. Supplementary Information The online version contains supplementary material available at 10.1186/s12889-023-16881-4.

Table S4).Using the quadratic polynomial models, we next tried to relax some of the main default constraints implemented by Mplus.Because the variances of BMI in each wave were different, we assumed heteroscedasticity within BMI and set the BMI residual variance (i.e.error) to be different across waves.Allowing the residual variances/errors to differ across the classes improved model fit BIC by between 23 and 151 (Model 5 vs. Model 2).We then attempted to extend Model 2 to incl ude a withinclass autocorrelation structure for the residual variances/errors.The final model included the class regression of the BMI on BMI t -1 to build the autoregressive correlation model (AR (1)).This improved model fit by between 153 and 687 (Model 6 vs. Model 2).Though improved model fit by between 41 and 359 (Model 4 vs.Model 2), the model fitting of the unconstrained model (model 4) was not better than that of final model ( Model 6).In summary, the best-fitting model covered a quadratic polyno mial function of age, within-class heteroscedastic errors, and a first-order autoregressive structure (AR1) to model auto -correlation.
Then the model including a quadratic polynomial function of age, within -class heteroscedastic errors, and a first-order autoregressive structure (AR1) was run for 1 -7 class solutions.In the modeling process, we included sex as covariate to adjust the pattern difference between sexes.To avoid convergence at local minima, we performed 1000 random starts (for 20 iterations), of which the best 200 models (according to log -likelihood) were run to completion (STARTS = 2000 200;STITERATIONS = 50).
father's and participants' occupation were categorized into high (social classes I -Ⅱ), medium (social classes Ⅲ -Ⅳ) and low (social class Ⅴ).The three individual SEP indicators were each coded 0 (low)-2(high), and all indicators were summed, ranging from 0 to 6, with higher values corresponding to greater life -course advantage.

a
Cumulative socioeconomic score (range 0-6) is calculated by summing all SEP indicators, including father's occupational position, participant's education and adult occupational position.Each SEP indicator was a 3-level variable with values ranging from 0 ( low) to 2 (high).b Model 1: Gender +residence +age; Model 2: Model 1+ change in smoking and drinking; Model 3: Model 1+ change in OPAL; Model 4: Model 1+ change in TDEI; Model 5: Model 1 + change in smoking, drinking, OPAL, and TDEI.ref: reference; OPAL: occupational physical activity level; TDEI: total daily energy intake.

a
Cumulative socioeconomic score (range 0 -6) is calculated by summing all SE P indicators, including father's occupational position, participant's education and adult occupational position.Each SEP indicator was a 3-level variable with values ranging from 0 (low) to 2 (high).b Model 1: Gender +residence +age; Model 2: Model 1+ change in smoking and drinking; Model 3: Model 1+ change in OPAL; Model 4: Model 1+ change in TDEI; Model 5: Model 1 + change in smoking, drinking, OPAL, and TDEI.ref: reference; OPAL: occupational physical activity level; TDEI: total daily energy intake.

a
Mo de l 1 : Ge nd e r + re s i de nc e + a ge ; M od el 2 : M od el 1+ ch an g e in s mo ki ng an d d rink i ng ; M od el 3: M od e l 1+ ch an ge in OPAL ; M od el 4 : M od el 1 + cha n ge in T DE I; Mod e l 5 : Mod el 1 + c ha ng e i n s m ok in g, d ri nk in g, OP AL , a nd T DE I. re f: re fe ren c e; O PAL : o c cu pa ti on a l p h y si c a l a ct iv i ty l ev e l; T DE I: t ot al da i ly en e rg y i nt ak e .b P a rt ic ip an t s ' oc c up at io n we re c a te go ri ze d i nt o hi gh (s oc i a l c la s s e s I -Ⅱ ), m e di u m (s o c ia l c l a s se s Ⅲ -Ⅳ ) a nd l ow (s o c i al cl a s s Ⅴ ).Adu l t edu c at io n wa s g ro up ed in to h ig h (≥ 1 2 y ea rs fo rm a l e du ca t ion ), m e di u m (8 -11 y e a rs fo rm a l ed u ca ti on ), an d lo w (< 8 y ea rs fo rm a l e du c at io n )

a
Figure S1.Theoretical model of the association between SEP and obesity based on a directed acyclic graph (DAG)

Figure S6 .
Figure S6.The association between father's occupation and BMI trajectories (before & after Multiple Imputation)

Figure S7 .
Figure S7.The association between adult education and BMI trajectories (before & after Multiple Imputation)

Table S7 . Characteristics of participants in cluded and excluded in the analysis a
a Data were expressed as numbers (percentages).Non -normally distributed data like baseline age was reported as median (IQR).BMI: body mass index; OPAL: occupational physical activity level; TDEI: total daily energy intake.

Table S14 . Details of chronic diseases of participants at the baseline (n=121)
Note: There were 14 participants with two or more chronic diseases .

Table S15 . Details of missing covariates in the sample
OPAL: occupational physical activity level; TDEI: total daily energy intake.

Table S16 . The comparison of the mixture models (GMM & GMM adjusted for the covariates )
a The model with quadratic polynomial function of age + Residual variances+ inclusion autoregression structure (AR1)